Chee-wooi Ten (ECE, ICC–CPS) is the co-author of a paper recently published in the IEEE Transactions on Smart Grid journal. The paper is titled, “Implementation of Risk-Aggregated Substation Testbed Using Generative Adversarial Networks.”
Co-authors of the paper are Zhiyuan Yang, Shipeng Zhang, Ting Liu, Xueyue Pang, and Hao Sun.
Abstract: Capturing the anomalies of a cyber system in power control networks would promote operational awareness. Correlation of such events, e.g., intrusion attempts, traffic flow, and other signatures, together with control alarm events gives operators an in-depth understanding in order to make an informed decision. This paper proposes a threat inference framework to promote real-time vulnerability assessment associated with cyber intrusions on power communication networks. Wasserstein Generative Adversarial Networks (WGAN) is proposed to estimate the performance of the adversarial model. Additionally, a machinelearning framework is introduced to model the filtering process of the security devices, i.e., firewalls, isolation, and encryption devices, and the posterior fitting method is incorporated to establish an accurate probabilistic formulation. Finally, a testbed is established to coordinate system evaluation. Verification of the intrusion model is part of the implementation to quantify system risks based on the anomalies using (1) the open-source emulator, and (2) an externally imported system analyzer to characterize resulting impacts. The proposed framework is validated with extensive studies of substation networks using real-world settings.
Citation: Yang, Zhiyuan & Zhang, Shipeng & Ten, Chee-Wooi & Liu, Ting & Pang, Xueyue & Sun, Hao. (2022). Implementation of Risk-Aggregated Substation Testbed Using Generative Adversarial Networks. IEEE Transactions on Smart Grid. 1-1. 10.1109/TSG.2022.3192522.